class_docstr: This class defines the forward method for vector quantization. As VQ is not differentiable, it returns a RuntimeError in case `.grad()` is called. Refer to `VectorQuantizationStraightThrough` for a straight_through estimation of the gradient for the VQ operation.
docstr: Handles error in case grad() is called on the VQ operation. 
docstr_tok: ["Handles","error","in","case","grad","(",")","is","called","on","the","VQ","operation","."]
code: def backward(ctx, grad_output):
      """Handles error in case grad() is called on the VQ operation."""
      raise RuntimeError(
          "Trying to call `.grad()` on graph containing "
          "`VectorQuantization`. The function `VectorQuantization` "
          "is not differentiable. Use `VectorQuantizationStraightThrough` "
          "if you want a straight-through estimator of the gradient."
      )
  
imports: import torch
  import torch.nn
  from torch.autograd import Function
label_desc: Use image node_id 2 for calling the VectorQuantization obj's underlying member method code with example usage: obj.backward(ctx, grad_output) without return types